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Neural Computing and Applications
Publishing model:
Hybrid

Neural Computing and Applications - Topical Collection on Large Language Models for Financial Services

The special issue aims to leverage the use of pretrained large language models (LLMs) in the realm of financial services - a growing field where LLMs are increasingly playing a substantial role in financial forecasting, risk management, and sentiment analysis. Furthermore, LLMs are establishing new practices in automating the interpretation of financial reports, thus, extracting crucial insights that improve informed business decision-making.

The issue serves as a medium for scholars, practitioners, and industry specialists across the globe to disseminate fresh views, exchange scholarly insights, and deliberate over the trials and potentialities tied to the utilization of pretrained LLMs within financial services. This special issue intends to explore two dominant threads: 1) possible engagements and optimal practices of pretrained LLMs within financial services, and 2) the challenges that need to be surmounted to render these LLMs as efficient, effective, and reliable resources.

In addition to research articles, the special issue will also welcome review papers led by eminent researchers and industry connoisseurs. It is also geared towards accommodating panel discussions reviewing contemporary trends and hurdles in the discipline. The special issue, therefore, invites submissions from academia and industry alike - from researchers, practitioners, and industry mavens who aspire to contribute to their work and be part of this dynamic discourse.

Guest Editors

Shuoling Liu (Lead Guest Editor), E Fund Management Co. Ltd, liushuoling@efunds.com.cn (this opens in a new tab)
Qiang Yang, WeBank/HKUST, qyang@cse.ust.hk (this opens in a new tab)
Liyuan Chen, E Fund Management Co. Ltd, chenly@efunds.com.cn (this opens in a new tab)
Yongpeng Tang, E Fund Management Co. Ltd, typ@efunds.com.cn (this opens in a new tab)
Xueyang Wu, Flaiverse, xwuba@connect.ust.hk (this opens in a new tab)
Qian Xu, HKUST, qianxu@hkust-gz.edu.cn (this opens in a new tab)

Manuscript Submission Deadline: 31st March 2024

Peer Review Process

All the papers will go through peer review,  and will be reviewed by at least two reviewers. A thorough check will be completed, and the guest editor will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue (with at least 60% difference from the original works).

Submission Guidelines

Paper submissions for the special issue should strictly follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines (this opens in a new tab)). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).

Manuscripts must be submitted to the journal online system at https://www.editorialmanager.com/ncaa/default.aspx (this opens in a new tab) or via the 'Submit manuscript' button on the journal homepage.
Authors should select “TC: Large Language Models for Financial Services” during the submission step ‘Additional Information’.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  
Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with  Help and Support (this opens in a new tab).
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